Abstract
We present a novel method of reducing the training time by learning parameters of a model at hand in compressed parameter space. In compressed parameter space the parameters of the model are represented by fewer parameters, and hence training can be faster. After training, the parameters of the model can be generated from the parameters in compressed parameter space. We show that for supervised learning, learning the parameters of a model in compressed parameter space is equivalent to learning parameters of the model in compressed input space. We have applied our method to a supervised learning domain and show that a solution can be obtained at much faster speed than learning in uncompressed parameter space. For reinforcement learning, we show empirically that searching directly the parameters of a policy in compressed parameter space accelerates learning.
This work was supported by the German Bundesministerium für Wirtschaft und Technologie (BMWi, grant FKZ 50 RA 1012 and grant FKZ 50 RA 1011).
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References
Calderbank, R., Jafarpour, S., Schapire, R.: Compressed learning: Universal sparse dimensionality reduction and learning in the measurement domain. Technical report (2009)
Davenport, M.A., Duarte, M.F., Wakin, M.B., Laska, J.N., Takhar, D., Kelly, K.F., Baraniuk, R.G.: The smashed filter for compressive classification and target recognition. In: Proceedings of Computational Imaging V at SPIE Electronic Imaging, San Jose, CA (January 2007)
Donoho, D.L.: Compressed sensing. IEEE Transactions on Information Theory 52(4), 1289–1306 (2006)
Gomez, F.J., Miikkulainen, R.: Robust non-linear control through neuroevolution. Technical Report AI-TR-03-303, Department of Computer Sciences, The University of Texas, Austin, USA (2002)
Gomez, F.J., Schmidhuber, J., Miikkulainen, R.: Efficient Non-linear Control Through Neuroevolution. In: Fürnkranz, J., Scheffer, T., Spiliopoulou, M. (eds.) ECML 2006. LNCS (LNAI), vol. 4212, pp. 654–662. Springer, Heidelberg (2006)
Hansen, N., Ostermeier, A.: Completely derandomized self-adaptation in evolution strategies. Evolutionary Computation 9(2), 159–195 (2001)
Haupt, J., Castro, R., Nowak, R., Fudge, G., Yeh, A.: Compressive sampling for signal classification. In: Proceedings of the 40th Asilomar Conference on Signals, Systems and Computers, pp. 1430–1434 (2006)
Kassahun, Y., de Gea, J., Edgington, M., Metzen, J.H., Kirchner, F.: Accelerating neuroevolutionary methods using a kalman filter. In: Proceedings of the 10th Annual Conference on Genetic and Evolutionary Computation (GECCO), pp. 1397–1404. ACM, New York (2008)
Koutník, J., Gomez, F., Schmidhuber, J.: Searching for minimal neural networks in fourier space. In: Baum, E., Hutter, M., Kitzelnmann, E. (eds.) Proceedings of the Third Conference on Artificial General Intelligence (AGI), pp. 61–66. Atlantic Press (2010)
Koutník, J., Gomez, F.J., Schmidhuber, J.: Evolving neural networks in compressed weight space. In: Proceedings of Genetic and Evolutionary Computation Conference (GECCO), pp. 619–626. ACM, New York (2010)
Maillard, O., Munos, R.: Compressed least-squares regression. In: Bengio, Y., Schuurmans, D., Lafferty, J., Williams, C.K.I., Culotta, A. (eds.) Advances in Neural Information Processing Systems (NIPS), pp. 1213–1221 (2009)
Schaul, T., Schmidhuber, J.: Towards Practical Universal Search. In: Proceedings of the Third Conference on Artificial General Intelligence (AGI), Lugano (2010)
Velez, D.R., White, B.C., Motsinger, A.A., Bush, W.S., Ritchie, M.D., Williams, S.M., Moore, J.H.: A balanced accuracy function for epistasis modeling in imbalanced datasets using multifactor dimensionality reduction. Genetic Epidemiology 31(4), 306–315 (2007)
Zander, T.O., Kothe, C.: Towards passive brain computer interfaces: applying brain computer interface technology to human machine systems in general. Journal of Neural Engineering 8(2), 025005 (2011)
Zhou, S., Lafferty, J.D., Wasserman, L.A.: Compressed regression. In: Platt, J.C., Koller, D., Singer, Y., Roweis, S.T. (eds.) Advances in Neural Information Processing Systems (NIPS), Curran Associates, Inc. (2008)
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Kassahun, Y., Wöhrle, H., Fabisch, A., Tabie, M. (2012). Learning Parameters of Linear Models in Compressed Parameter Space. In: Villa, A.E.P., Duch, W., Érdi, P., Masulli, F., Palm, G. (eds) Artificial Neural Networks and Machine Learning – ICANN 2012. ICANN 2012. Lecture Notes in Computer Science, vol 7553. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33266-1_14
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DOI: https://doi.org/10.1007/978-3-642-33266-1_14
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